A theory-grounded text message–based intervention to reduce sedentary behaviour in university students
Bibliographic record
Abstract
Objective: To evaluate the feasibility and acceptability of a theory-grounded, text message–based intervention targeting sedentary behaviour among university students. Design: Single-group repeated measures design. Setting: Post-secondary institution in British Columbia, Canada. Methods: Data concerning students’ sedentary behaviour were collected via online survey completed at three time points over the course of one university semester: baseline (T1), post-intervention (T2) and 2-week follow-up (T3). The 6-week intervention comprised four weekly text messages delivered to participants’ mobile devices. Participants’ attitudes regarding the intervention were evaluated together with other measures including constructs in the Health Action Process Approach (HAPA). Sedentary behaviour and physical activity were measured using the Physical Activity and Sedentary Behaviour Questionnaire (PASB-Q). Results: The intervention was generally well received by participants. Preliminary, observational data suggest some indices of user experience were statistically associated with behavioural outcomes and may inform future work. Hours per week of sedentary behaviour did not change across time points, whereas minutes per week of physical activity decreased significantly from baseline to follow-up. Conclusion: While study findings suggest minor modifications to the intervention may improve participants’ engagement, we demonstrated overall that a theory-grounded, text message–based intervention to reduce sedentary behaviour can be feasibly implemented. The efficacy of this intervention should be tested through a randomised control trial with a representative sample of the student population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".